1. Field of the Invention
Embodiments of the present invention generally relate to image acquisition system and, more particularly, to adaptive image acquisition systems.
2. Description of the Related Art
Unmanned aerial vehicles (UAVs) are widely used as platforms for surveillance in battlefield and other environments. Generally, a UAV carries a camera and other sensor payloads and relays data collected by the sensor payloads through a radio link to an operator. The operator controls the flight path of the UAV and the view captured by the payloads from a remote location via a radio link to the UAV. The operator uses images from the camera to guide the UAV over the area to be monitored.
To achieve persistent surveillance using UAVs with current technology requires too many UAVs and operators to cover even a limited coverage area. For example, given a surveillance area of 3 sq-km, persistent surveillance requires 30 UAVs (Surveillance Area/Sensor Area Coverage=3.0/0.10) to identify vehicle type or track vehicles within the area. If the task is to have precise monitoring (i.e., ID vehicles and detect people) it would require 225 UAVs (3.0/0.016=225). Obviously, this is not a viable solution. What happens today is that either an operator concentrates on a single target (and therefore misses other intelligence opportunities) or the operator scans over the entire region. Such re-scanning causes a delayed target revisit period, e.g., as much as 20 minutes.
Direct-operator-control of imaging sensors, such as cameras, significantly limits the capabilities of an imaging system. Since a human needs time to perceive and understand the content of an image or a video sequence, the imaging system is operated at a speed much less than of the capability of the system. For example, even if a camera can take 30 pictures of 30 different places in one second, the camera operator can not understand them, let alone control a camera to take 30 pictures of 30 different places.
Because of these limitations, current UAVs monitor large areas but with very low update rates for regions that they are not being immediately imaged, and for the targets that are being imaged, the update rate is typically much too high (30-60 times a second). Almost all of the information is redundant and adds little or no intelligence value. Additionally, intelligence opportunities often occur in a bursty manner. Long periods generally exist between consecutive events. This can cause a significant decrease in operators' attention and thereby increase the chance of missed targets and events.
Similarly, image analysts face a sea of imagery data, most of which have little intelligence value. Searching, exploiting and navigating through the vast amount of data by analysts are both inefficient and error-prone. It will also increase the response time and cause unnecessary delay.
In addition, the total video bandwidth to cover the entire region simultaneously would be prohibitive. Each compressed video sensor would require at least 7 Mbits/sec. A total coverage would therefore require 7×225=1,575 Mbits/sec. This would overwhelm any available communications link.
To achieve wide area persistent surveillance, motion-imagery acquisition systems operate under severe constraints: limited bandwidth for communication, continuous operation over long periods, and unpredictable changes of tasks and environment. Thus, it is desirable that image acquisition be self-adaptive and fully automated, facilitating continuous capture of all events and monitoring of all targets by providing persistent surveillance of a large area at both high spatial and temporal resolution. In addition, intelligence has to be extracted from the huge amount of data acquired by a persistent surveillance system with low latency.
Therefore, there is a need in the art for a persistent surveillance technique that overcomes the deficiencies of the prior art.